The main goal of this core is to develop computational procedures for the analysis of high-throughput data from T cell receptor (TCR) or B cell receptor (BCR) repertoire, RNA-seq and flow cytometry, and apply them to study the tissue specific data generated in the proposed projects. TCR and BCR repertoire sequencing provides information about clonal lineage and tissue-specific expansion of T / B cell populations, which is a key component to test the hypotheses in Projects 1, 2 and 4. RNA-seq is a powerful approach to profile gene expression and alternative splicing, which are important for studying the specific states of lymphocytes and local environment of different tissues, and will be applied extensively in Projects 1, 2 and 3. For all projects, a streamlined procedure to analyze large-scale multidimensional flow cytometry data is crucial so we can separate the different immune cell populations we wish to; study precisely. We have three specific service aims in this core: (1) Establish and apply computational approaches to analyze T and B cell receptor repertoire sequencing data. We have established an in-house bioinformatics pipeline to analyze massive accounts of TCR and BCR repertoire sequencing data from lllumina HiSeq or MiSeq platforms. For this part ofthe core, we will continue to develop analytical methods for characterizing repertoire diversity and comparing of repertoire of different tissues across individuals. We will then perform the computational and mathematical analysis of TCR and BCR repertoires for Projects 1, 2 and 4. (2) Establish and apply computational approaches to analyze RNA-seq data to find signatures of expressions that distinguish cell linages and tissues. We have a mature analytical pipeline for RNA-seq data at Columbia Genome Center Next-Generation Sequencing Laboratory. The field is in active development; newer methods are being published. For this part of the core, we will assess the performance of new and existing methods, and optimize the procedure for finding expression signatures that define local environment in different tissues and immune cell states. We will perform the computational analysis for Projects 1 through 3. (3) Establish and apply computational approaches to analyze high-throughput flow cytometry data. The purpose is to find not only canonical populations of immune cells, but also discover novel or rare populations from multi- dimensional flow cytometry data. We will establish an analytical pipeline based on existing and in- development R/Bioconductor tools.